New approaches to molecular cancer therapeutics

Cancer drug development is leading the way in exploiting molecular biological and genetic information to develop "personalized" medicine. The new paradigm is to develop agents that target the precise molecular pathology driving the progression of individual cancers. Drug developers have benefited from decades of academic cancer research and from investment in genomics, genetics and automation; their success is exemplified by high-profile drugs such as Herceptin (trastuzumab), Gleevec (imatinib), Tarceva (erlotinib) and Avastin (bevacizumab). However, only 5% of cancer drugs entering clinical trials reach marketing approval. Cancer remains a high unmet medical need, and many potential cancer targets remain undrugged. In this review we assess the status of the discovery and development of small-molecule cancer therapeutics. We show how chemical biology approaches offer techniques for interconnecting elements of the traditional linear progression from gene to drug, thereby providing a basis for increasing speed and success in cancer drug discovery.     

Accurate and Reliable Cancer Classification Based on Probabilistic Inference of Pathway Activity

With the advent of high-throughput technologies for measuring genome-wide expression profiles, a large number of methods have been proposed for discovering diagnostic markers that can accurately discriminate between different classes of a disease. However, factors such as the small sample size of typical clinical data, the inherent noise in high-throughput measurements, and the heterogeneity across different samples, often make it difficult to find reliable gene markers. To overcome this problem, several studies have proposed the use of pathway-based markers, instead of individual gene markers, for building the classifier. Given a set of known pathways, these methods estimate the activity level of each pathway by summarizing the expression values of its member genes, and use the pathway activities for classification. It has been shown that pathway-based classifiers typically yield more reliable results compared to traditional gene-based classifiers. In this paper, we propose a new classification method based on probabilistic inference of pathway activities. For a given sample, we compute the log-likelihood ratio between different disease phenotypes based on the expression level of each gene. The activity of a given pathway is then inferred by combining the log-likelihood ratios of the constituent genes. We apply the proposed method to the classification of breast cancer metastasis, and show that it achieves higher accuracy and identifies more reproducible pathway markers compared to several existing pathway activity inference methods.     

Mathematical model for chemotherapeutic drug efficacy in arresting tumour growth based on the cancer stem cell hypothesis

OBJECTIVE: Cancer stem cells have been identified as the growth root for various malignant tumours and are thought to be responsible for cancer recurrence following treatment. MATERIALS AND METHODS: Here, a predictive mathematical model for the cancer stem cell hypothesis is used to understand tumour responses to chemotherapeutic drugs and judge the efficacy of treatments in arresting tumour growth. The impact of varying drug efficacies on different abnormal cell populations is investigated through the kinetics associated with their decline in response to therapy. RESULTS AND CONCLUSIONS: The model predicts the clinically established 'dandelion phenomenon' and suggests that the best response to chemotherapy occurs when a drug targets abnormal stem cells. We compare continuous and periodic drug infusion. For the latter, we examine the relative importance of the drug cell-kill rate and the mean time between successive therapies, to identify the key attributes for successful treatment.     

In silico drug combination discovery for personalized cancer therapy

Background

Drug combination therapy, which is considered as an alternative to single drug therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug combination therapies are widely used in the clinic for hypertension, asthma, and AIDS, they have also been proposed for the treatment of cancer. However, it is difficult to select and experimentally evaluate effective combinations because not only is the number of cancer drug combinations extremely large but also the effectiveness of drug combinations varies depending on the genetic variation of cancer patients. A computational approach that prioritizes the best drug combinations considering the genetic information of a cancer patient is necessary to reduce the search space.

Results

We propose an in-silico method for personalized drug combination therapy discovery. We predict the synergy between two drugs and a cell line using genomic information, targets of drugs, and pharmacological information. We calculate and predict the synergy scores of 583 drug combinations for 31 cancer cell lines. For feature dimension reduction, we select the mutations or expression levels of the genes in cancer-related pathways. We also used various machine learning models. Extremely Randomized Trees (ERT), a tree-based ensemble model, achieved the best performance in the synergy score prediction regression task. The correlation coefficient between the synergy scores predicted by ERT and the actual observations is 0.738. To compare with an existing drug combination synergy classification model, we reformulate the problem as a binary classification problem by thresholding the synergy scores. ERT achieved an F1 score of 0.954 when synergy scores of 20 and -20 were used as the threshold, which is 8.7% higher than that obtained by the state-of-the-art baseline model. Moreover, the model correctly predicts the most synergistic combination, from approximately 100 candidate drug combinations, as the top choice for 15 out of the 31 cell lines. For 28 out of the 31 cell lines, the model predicts the most synergistic combination in the top 10 of approximately 100 candidate drug combinations. Finally, we analyze the results, generate synergistic rules using the features, and validate the rules through the literature survey.

Conclusion

Using various types of genomic information of cancer cell lines, targets of drugs, and pharmacological information, a drug combination synergy prediction pipeline is proposed. The pipeline regresses the synergy level between two drugs and a cell line as well as classifies if there exists synergy or antagonism between them. Discovering new drug combinations by our pipeline may improve personalized cancer therapy.

     

基于多组学数据的早期宫颈鳞状细胞癌分类

从分子层面对泛癌进行研究已经得到了很大的进展,但是对宫颈鳞状细胞癌的分子分类研究仍然需要更多的探索.为了找到宫颈鳞状细胞癌潜在的子类,本文提出了一个基于多维组学数据的癌症亚型分类分析流程.通过统计学方法对癌症基因组图谱(The Cancer Genome Atlas,TCGA)宫颈鳞状细胞癌的mRNA表达数据、小分子核糖核酸(microRNA,miRNA)表达数据、DNA甲基化数据以及拷贝数变异数据4个维度包含的分子进行筛选,然后对筛选后的分类特征进行整合聚类,进一步筛选能够区分不同子类的关键分类特征,并使用这些关键分类特征建立宫颈鳞状细胞癌分类模型.本研究为宫颈鳞状细胞癌分子层面子类的识别提供了分析流程,得到了两个临床生存水平具有显著性差异的宫颈鳞状细胞癌子类,并确定了8个宫颈鳞状细胞癌的关键分类特征.本研究中识别的宫颈鳞状细胞癌子类和关键分类特征为宫颈鳞状细胞癌早期分类及分类标志物的鉴定提供了重要参考.     

CD166 promotes the cancer stem-like properties of primary epithelial ovarian cancer cells

Cancer stem cells (CSCs) or tumor-initiating cells are thought to play critical roles in tumorigenesis, metastasis, drug resistance, and tumor recurrence. For the diagnosis and targeted therapy of CSCs, the molecular identity of biomarkers or therapeutic targets for CSCs needs to be clarified. In this study, we identified CD166 as a novel marker expressed in the sphere-forming CSC population of A2780 epithelial ovarian cancer cells and primary ovarian cancer cells. The CD166⁺ cells isolated from A2780 cells and primary ovarian cancer cells highly expressed CSC markers, including ALDH1a1, OCT4, and SOX2, and ABC transporters, which are implicated in the drug resistance of CSCs. The CD166⁺ cells exhibited enhanced CSC-like properties, such as increased sphere-forming ability, cell migration and adhesion abilities, resistance to conventional anti-cancer drugs, and high tumorigenic potential in a xenograft mouse model. Knockdown of CD166 expression in the sphere-forming ovarian CSCs abrogated their CSC-like properties. Moreover, silencing of CD166 expression in the sphere-forming CSCs suppressed the phosphorylation of focal adhesion kinase, paxillin, and SRC. These results suggest that CD166 plays a key role in the regulation of CSC-like properties and focal adhesion kinase signaling in ovarian cancer.     

Incorporating pathologists' criteria of malignancy into the evolutionary model for cancer development

A wide variety of alterations in cell and tissue structure still form the basis for cancer diagnosis by pathologists. Cancer development is recognized to be an evolutionary process [Foulds, 1954; Cairns, 1975; Nowell, 1976; Sager, 1982; Tomlinson et al., 1996; Cahill et al., 1999; Tomlinson and Bodmer, 1999], but the phenotypic changes diagnostic of cancer (pathologists' "criteria of malignancy") have not been integrated into the existing evolutionary framework. Since phenotypic changes bear an important relationship to the genetic and physiologic changes underlying Darwinian evolution, we propose that diagnostic structural alterations also bear an important and predictable relation to both the cancer genes and the functional alterations active at any particular step in the development of a cancer. Cancer genes are predicted to mediate the acquisition of cellular-level diagnostic criteria and the diagnostic cellular-level structural changes should reflect in a useful manner the altered cell physiology required for the cell to achieve increased "cellular fitness" at any particular step of colonal evolution. Tissue-level criteria of malignancy should relate less directly to specific cancer genes, but tissue-level criteria should still provide essential insight into the interplay of the altered cellular fitness with the constraints imposed by the cells' microenvironment. The evolutionary framework allows tissue-level criteria of malignancy to be expressed in terms of viable hypotheses for the mechanism of clonal expansion at any particular step in cancer development. This approach to conveying the tissue-level criteria of malignancy complements pattern recognition approaches to diagnosis, and establishes common ground between pathology and cell biology. When viewed from this perspective, the functions of cancer genes appear quite different from those predicted by the "Gatekeeper, Caretaker" or "Hallmarks of Cancer" models. Finally, a full evolutionary framework incorporating the criteria of malignancy restores congruity between the histogenetic classification and the emerging molecular classification of cancer.     

Topology-based cancer classification and related pathway mining using microarray data

Cancer classification is the critical basis for patient-tailored therapy, while pathway analysis is a promising method to discover the underlying molecular mechanisms related to cancer development by using microarray data. However, linking the molecular classification and pathway analysis with gene network approach has not been discussed yet. In this study, we developed a novel framework based on cancer class-specific gene networks for classification and pathway analysis. This framework involves a novel gene network construction, named ordering network, which exhibits the power-law node-degree distribution as seen in correlation networks. The results obtained from five public cancer datasets showed that the gene networks with ordering relationship are better than those with correlation relationship in terms of accuracy and stability of the classification performance. Furthermore, we integrated the ordering networks, classification information and pathway database to develop the topology-based pathway analysis for identifying cancer class-specific pathways, which might be essential in the biological significance of cancer. Our results suggest that the topology-based classification technology can precisely distinguish cancer subclasses and the topology-based pathway analysis can characterize the correspondent biochemical pathways even if there are subtle, but consistent, changes in gene expression, which may provide new insights into the underlying molecular mechanisms of tumorigenesis.     

The Role of Intercellular Communication in Cancer Progression

Although understanding of the molecular biology of cancer has advanced and medicine has an impressive arsenal of chemotherapeutic drugs, the problem of tumor resistance to individual drugs and drug combinations has not yet been resolved. Known mechanisms of cancer chemoresistance do not explain the reason for such a phenomenon as “apoptosis-induced proliferation,” where cells dying under the effect of the therapy secrete some signaling molecules into the extracellular medium to promote proliferation, survival, and acquisition of a more aggressive phenotype of neighboring cancer cells. The nature of this unexpected phenomenon is only now beginning to be partially clarified, but the nature of such signaling between apoptotic cancer cells and their neighboring cells remains largely unknown. For this reason, in this review we discuss currently known types of intercellular communication of tumor cells, give specific examples of important secreted molecules involved in signaling between cancer cells, and describe possible cell interactions contributing to the progress of cancer outgrowth.     

Noninvasive detection and imaging of molecular markers in live cardiomyocytes derived from human embryonic stem cells

Raman microspectroscopy (RMS) was used to detect and image molecular markers specific to cardiomyocytes (CMs) derived from human embryonic stem cells (hESCs). This technique is noninvasive and thus can be used to discriminate individual live CMs within highly heterogeneous cell populations. Principal component analysis (PCA) of the Raman spectra was used to build a classification model for identification of individual CMs. Retrospective immunostaining imaging was used as the gold standard for phenotypic identification of each cell. We were able to discriminate CMs from other phenotypes with >97% specificity and >96% sensitivity, as calculated with the use of cross-validation algorithms (target 100% specificity). A comparison between Raman spectral images corresponding to selected Raman bands identified by the PCA model and immunostaining of the same cells allowed assignment of the Raman spectral markers. We conclude that glycogen is responsible for the discrimination of CMs, whereas myofibril proteins have a lesser contribution. This study demonstrates the potential of RMS for allowing the noninvasive phenotypic identification of hESC progeny. With further development, such label-free optical techniques may enable the separation of high-purity cell populations with mature phenotypes, and provide repeated measurements to monitor time-dependent molecular changes in live hESCs during differentiation in vitro.     

Dynamics of resistance development to imatinib under increasing selection pressure: a combination of mathematical models and in vitro data

In the last decade, cancer research has been a highly active and rapidly evolving scientific area. The ultimate goal of all efforts is a better understanding of the mechanisms that discriminate malignant from normal cell biology in order to allow the design of molecular targeted treatment strategies. In individual cases of malignant model diseases addicted to a specific, ideally single oncogene, e.g. Chronic myeloid leukemia (CML), specific tyrosine kinase inhibitors (TKI) have indeed been able to convert the disease from a ultimately life-threatening into a chronic disease with individual patients staying in remission even without treatment suggestive of operational cure. These developments have been raising hopes to transfer this concept to other cancer types. Unfortunately, cancer cells tend to develop both primary and secondary resistance to targeted drugs in a substantially higher frequency often leading to a failure of treatment clinically. Therefore, a detailed understanding of how cells can bypass targeted inhibition of signaling cascades crucial for malignant growths is necessary. Here, we have performed an in vitro experiment that investigates kinetics and mechanisms underlying resistance development in former drug sensitive cancer cells over time in vitro. We show that the dynamics observed in these experiments can be described by a simple mathematical model. By comparing these experimental data with the mathematical model, important parameters such as mutation rates, cellular fitness and the impact of individual drugs on these processes can be assessed. Excitingly, the experiment and the model suggest two fundamentally different ways of resistance evolution, i.e. acquisition of mutations and phenotype switching, each subject to different parameters. Most importantly, this complementary approach allows to assess the risk of resistance development in the different phases of treatment and thus helps to identify the critical periods where resistance development is most likely to occur.     

Accounting for Errors in Data Improves Divergence Time Estimates in Single-cell Cancer Evolution

Single-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, thereby preserving the information about the origin of the sequences. However, single-cell data are more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show that modeling errors increase the accuracy of relative divergence times and substitution parameters. We reconstruct the phylogenetic history of a colorectal cancer patient and a healthy patient from single-cell DNA sequencing data. We find that the estimated times of terminal splitting events are shifted forward in time compared to models which ignore errors. We observed that not accounting for errors can overestimate the phylogenetic diversity in single-cell DNA sequencing data. We estimate that 30–50% of the apparent diversity can be attributed to error. Our work enables a full Bayesian approach capable of accounting for errors in the data within the integrative Bayesian software framework BEAST2.     

The Worst Drug Rule Revisited: Mathematical Modeling of Cyclic Cancer Treatments

In drug treatments of cancer, cyclic treatment strategies are characterized by alternating applications of two (or more) different drugs, given one at a time. One of the main problems of drug treatment in cancer is associated with the generation of drug resistance by mutations of cancerous cells. We use mathematical methods to develop general guidelines on optimal cyclic treatment scheduling, with the aim of minimizing the resistance generation. We define a condition on the drugs’ potencies which allows for a relatively successful application of cyclic therapies. We find that the best strategy is to start with the stronger drug, but use longer cycle durations for the weaker drug. We further investigate the situation where a degree of cross-resistance is present, such that certain mutations cause cells to become resistant to both drugs simultaneously. We show that the general rule (best-drug-first, worst-drug-longer) is unchanged by the presence of cross-resistance. We design a systematic method to test all strategies and come up with the optimal timing and drug order. The role of various constraints in the optimal therapy design, and in particular, suboptimal treatment durations and drug toxicity, is considered. The connection with the “worst drug rule” of Day (Cancer Res. 46:3876, 1986b) is discussed.     

NEDD4 is involved in acquisition of epithelial-mesenchymal transition in cisplatin-resistant nasopharyngeal carcinoma cells

Nasopharyngeal carcinoma (NPC) is a highly invasive head-neck cancer derived from the nasopharyngeal epithelium, mainly prevalent in southern China and Southeast Asia. Radiotherapy and adjuvant cisplatin (DDP) chemotherapy are standard administrations applied in the treatment of NPC. However, resistance to chemotherapeutic drugs has recently become more common, resulting in worse treatment outcome for NPC therapy. To elucidate the underlying molecular basis of drug resistance to DDP in NPC cells, we examined the morphocytology, cell motility and molecular changes in DDP-resistant NPC cells with respect to epithelial-mesenchymal transition (EMT) features. We found that EMT is closely associated with DDP-induced drug resistance in NPC cells, as DDP-resistant cells displayed morphological and molecular markers changes consistent with EMT. Wound healing and Transwell Boyden chamber assays revealed an enhanced migration and invasion potential in DDP-resistant NPC cells. Mechanistically, upregulation of NEDD4 was observed to relate to EMT in DDP-resistant cells. More importantly, depletion of NEDD4 in resistant cells led to a partial reversion of EMT phenotypes to MET characteristics. These data suggest that NEDD4 is largely involved in EMT features and chemoresistance of NPC cancer cells. NEDD4 could be a novel therapeutic target to overcome drug resistance in successful administrations of NPC.     

Integrated analysis of pivotal biomarker of LSM1, immune cell infiltration and therapeutic drugs in breast cancer

The discovery of early diagnosis and prognostic markers for breast cancer can significantly improve survival and reduce mortality. LSM1 is known to be involved in the general process of mRNA degradation in complexes containing LSm subunits, but the molecular and biological functions in breast cancer remain unclear. Here, the expression of LSM1 mRNA in breast cancer was estimated using The Cancer Genome Atlas (TCGA), Oncomine, TIMER and bc‐GenExMiner databases. We found that functional LSM1 inactivation caused by mutations and profound deletions predicted poor prognosis in breast cancer (BRCA) patients. LSM1 was highly expressed in both BRCA tissues and cells compared to normal breast tissues/cells. High LSM1 expression is associated with poorer overall survival and disease‐free survival. The association between LSM1 and immune infiltration of breast cancer was assessed by TIMER and CIBERSORT algorithms. LSM1 showed a strong correlation with various immune marker sets. Most importantly, pharmacogenetic analysis of BRCA cell lines revealed that LSM1 inactivation was associated with increased sensitivity to refametinib and trametinib. However, both drugs could mimic the effects of LSM1 inhibition and their drug sensitivity was associated with MEK molecules. Therefore, we investigated the clinical application of LSM1 to provide a basis for sensitive diagnosis, prognosis and targeted treatment of breast cancer.     

Are cancer cells really softer than normal cells?

Solid tumours are often first diagnosed by palpation, suggesting that the tumour is more rigid than its surrounding environment. Paradoxically, individual cancer cells appear to be softer than their healthy counterparts. In this review, we first list the physiological reasons indicating that cancer cells may be more deformable than normal cells. Next, we describe the biophysical tools that have been developed in recent years to characterise and model cancer cell mechanics. By reviewing the experimental studies that compared the mechanics of individual normal and cancer cells, we argue that cancer cells can indeed be considered as softer than normal cells. We then focus on the intracellular elements that could be responsible for the softening of cancer cells. Finally, we ask whether the mechanical differences between normal and cancer cells can be used as diagnostic or prognostic markers of cancer progression.